As organizations grow and optimize business through AI, there is a pressing need to ensure robust governance of models used in business-critical applications and workflows. For both regulated and unregulated industries, it is crucial to incorporate data science best practices like model documentation, centralized monitoring of models, and tackling bias. Such a framework also enables organizations to meet government regulations and reduce overall risk from models in production.
In this session you will learn:
- Key challenges involved in governing ML for regulated and unregulated industries
- How to automatically uphold production models to the highest governance standards
- Why Freddie Mac, a leading home mortgage company, is leveraging DataRobot for automated compliance and governance
Speaker
Brian Bell Jr.
Senior Director of Product, AI Production, DataRobot
DataRobot is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively.
The generative AI space is changing quickly, and the flexibility, safety and security of DataRobot helps us stay on the cutting edge with a HIPAA-compliant environment we trust to uphold critical health data protection standards. We’re harnessing innovation for real-world applications, giving us the ability to transform patient care and improve operations and efficiency with confidence
DataRobot provides us with innovative ways to test new ideas. Given a problem and a dataset, DataRobot allows us to generate multiple prototypes 20% faster. And the process facilitates the learning evolution of our data scientists.
The value of having a single platform that pulls all the components together can’t be underestimated. Then there’s the combination of the technology and the collaborative DataRobot team. If either one of those wasn’t there, I would have looked elsewhere.